Experimental validation of in silico target predictions on synergistic protein targets. - Isidro Cortes-Ciriano; Alexios Koutsoukas; Olga Abian; Andreas Bender 002; Adrián Velázquez-Campoy (J. Cheminformatics, 2013)

Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets. - Gerard J. P. van Westen; Remco F. Swier; Isidro Cortes-Ciriano; Jörg K. Wegner; John P. Overington; Adriaan P. IJzerman; Herman W. T. van Vlijmen; Andreas Bender 002 (J. Cheminformatics, 2013)

Proteochemometric modeling in a Bayesian framework. - Isidro Cortes-Ciriano; Gerard J. P. van Westen; Eelke B. Lenselink; Daniel S. Murrell; Andreas Bender 002; Thérèse E. Malliavin (J. Cheminformatics, 2014)

Applications of proteochemometrics - from species extrapolation to cell line sensitivity modelling. - Isidro Cortes-Ciriano; Gerard J. P. van Westen; Daniel S. Murrell; Eelke B. Lenselink; Andreas Bender 002; Therese E. Malliavin (BMC Bioinform., 2015)

Identification of binding sites and favorable ligand binding moieties by virtual screening and self-organizing map analysis. - Emna Harigua-Souiai; Isidro Cortes-Ciriano; Nathan Desdouits; Thérèse E. Malliavin; Ikram Guizani; Michael Nilges; Arnaud Blondel; Guillaume Bouvier (BMC Bioinform., 2015)

Prediction of the potency of mammalian cyclooxygenase inhibitors with ensemble proteochemometric modeling. - Isidro Cortes-Ciriano; Daniel S. Murrell; Gerard J. P. van Westen; Andreas Bender 002; Thérèse E. Malliavin (J. Cheminformatics, 2015)

Chemically Aware Model Builder (camb): an R package for property and bioactivity modelling of small molecules. - Daniel S. Murrell; Isidro Cortes-Ciriano; Gerard J. P. van Westen; Ian Stott; Andreas Bender 002; Thérèse E. Malliavin; Robert C. Glen (J. Cheminformatics, 2015)

Proteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules. - Shardul Paricharak; Isidro Cortes-Ciriano; Adriaan P. IJzerman; Therese E. Malliavin; Andreas Bender 002 (J. Cheminformatics, 2015)

Comparing the Influence of Simulated Experimental Errors on 12 Machine Learning Algorithms in Bioactivity Modeling Using 12 Diverse Data Sets. - Isidro Cortes-Ciriano; Andreas Bender 002; Thérèse E. Malliavin (J. Chem. Inf. Model., 2015)

Improved Chemical Structure-Activity Modeling Through Data Augmentation. - Isidro Cortes-Ciriano; Andreas Bender 002 (J. Chem. Inf. Model., 2015)

Applications of proteochemometrics (PCM) : from species extrapolation to cell-line sensitivity modelling. (Applications de proteochemometrics : à partir de l'extrapolation des espèces à la modélisation de la sensibilité de la lignée cellulaire). - Isidro Cortes-Ciriano (2015)

Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel. - Isidro Cortes-Ciriano; Gerard J. P. van Westen; Guillaume Bouvier; Michael Nilges; John P. Overington; Andreas Bender 002; Thérèse E. Malliavin (Bioinform., 2016)

Bioalerts: a python library for the derivation of structural alerts from bioactivity and toxicity data sets. - Isidro Cortes-Ciriano (J. Cheminformatics, 2016)

Benchmarking the Predictive Power of Ligand Efficiency Indices in QSAR. - Isidro Cortes-Ciriano (J. Chem. Inf. Model., 2016)

Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Errors for Deep Neural Networks. - Isidro Cortes-Ciriano; Andreas Bender 002 (CoRR, 2018)

KekuleScope: improved prediction of cancer cell line sensitivity using convolutional neural networks trained on compound images. - Isidro Cortes-Ciriano; Andreas Bender 002 (CoRR, 2018)

Discovering Highly Potent Molecules from an Initial Set of Inactives Using Iterative Screening. - Isidro Cortés-Ciriano; Nicholas C. Firth; Andreas Bender 002; Oliver P. Watson (J. Chem. Inf. Model., 2018)

Conformal Regression for Quantitative Structure-Activity Relationship Modeling - Quantifying Prediction Uncertainty. - Fredrik Svensson; Natália Aniceto; Ulf Norinder; Isidro Cortes-Ciriano; Ola Spjuth; Lars Carlsson; Andreas Bender 002 (J. Chem. Inf. Model., 2018)

A decision-theoretic approach to the evaluation of machine learning algorithms in computational drug discovery. - Oliver P. Watson; Isidro Cortes-Ciriano; Aimee R. Taylor; James A. Watson (Bioinform., 2019)

Concepts and Applications of Conformal Prediction in Computational Drug Discovery. - Isidro Cortés-Ciriano; Andreas Bender 002 (CoRR, 2019)

KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images. - Isidro Cortes-Ciriano; Andreas Bender 002 (J. Cheminformatics, 2019)

Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Prediction Errors for Deep Neural Networks. - Isidro Cortés-Ciriano; Andreas Bender 002 (J. Chem. Inf. Model., 2019)

Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout. - Isidro Cortés-Ciriano; Andreas Bender 002 (J. Chem. Inf. Model., 2019)

QSAR-derived affinity fingerprints (part 2): modeling performance for potency prediction. - Isidro Cortés-Ciriano; Ctibor Skuta; Andreas Bender 002; Daniel Svozil (J. Cheminformatics, 2020)

QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping. - Ctibor Skuta; Isidro Cortes-Ciriano; Wim Dehaen; Pavel Kríz; Gerard J. P. van Westen; Igor V. Tetko; Andreas Bender 002; Daniel Svozil (J. Cheminformatics, 2020)

Genomic basis for RNA alterations in cancer. - Claudia Calabrese; Natalie R. Davidson; Deniz Demircioglu; Nuno A. Fonseca; Yao He; André Kahles; Kjong-Van Lehmann; Fenglin Liu; Yuichi Shiraishi; Cameron M. Soulette; Lara Urban; Liliana Greger; et al. (Nat., 2020)

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes. - Esther Rheinbay; Morten Muhlig Nielsen; Federico Abascal; Jeremiah Wala; Ofer Shapira; Grace Tiao; Henrik Hornshøj; Julian M. Hess; Randi Istrup Juul; Ziao Lin; Lars Feuerbach; Radhakrishnan Sabarinathan; et al. (Nat., 2020)

A semi-supervised learning framework for quantitative structure-activity regression modelling. - Oliver P. Watson; Isidro Cortes-Ciriano; James A. Watson (Bioinform., 2021)

ReConPlot: an R package for the visualization and interpretation of genomic rearrangements. - Jose Espejo Valle-Inclán; Isidro Cortés-Ciriano (Bioinform., 2023)